code: def doc_to_target(self, doc):
      # There's a short answer and a long answer. Based on the paper, I'm using the long answer.
      # short_answer = doc["annotations"]["short_answers"][0]["text"]
      long_answer_start = doc["annotations"]["long_answer"][0][
          "start_token"
      ]
      long_answer_end = doc["annotations"]["long_answer"][0][
          "end_token"
      ]
      long_answer_span = doc["document"]["tokens"]["token"][
          long_answer_start:long_answer_end
      ]
      long_answer_is_html = doc["document"]["tokens"]["is_html"][
          long_answer_start:long_answer_end
      ]
      long_answer_chars = [
          tok
          for (tok, is_html) in zip(
              long_answer_span, long_answer_is_html
          )
          if not is_html
      ]
      long_answer = " ".join(long_answer_chars)
      return long_answer
  
file_path: lm-evaluation-harness/lm_eval/tasks/naturalqs.py
imports: from lm_eval.base import Task
  from itertools import islice
label_desc: Use image node_id 10 for calling the NaturalQs obj's underlying member method code with example usage: obj.doc_to_target(doc) and returns: long_answer